This research aims at establishment of the system safe design support method by the integrated model based on a physical analogy rule to the complex system which consists of different systems, such as an electric system, a mechanical system, and a fluid systemIn the first stage, Heisei 13, the derivation method of system accident causes, the sensor allocation which detects component failure, and its diagnostic method were examined. It is necessary to take into consideration hardware, software, and human action as a cause of failure. An integrated system model is composed of hardware actions expressed by bond graph and software & human actions represented as input-and-output relation which changes the characteristics of the hardware. Cause-effect sequences which may lead to the system accident are derived based on the integrated model. Considering the effect of protective system, the system failure occurrence conditions are derived. The merits of the proposed method were confirmed throu
… Moregh a simple example of chemical plant. Moreover, the easy evaluation method of the system failure occurrence probability was proposed by considering component failure time order, and the merits were verified as compared with the Markov analysis. Furthermore, monitoring points required for the identification of any component failure can be obtained based on the integrated model presented in bond graph and the optimal inspection order was proposed, and the validity was confirmedIn the second stage, Heisei 14, the framework of safe computer-aided design was completed and comprehensive evaluation in a case analysis was performed. Moreover, the dynamic Bayesian network was considered as the diagnostic method using the framework of an integrated system model. For the depth in the defense which is the foundations of a system safe design, identification and its loss evaluation of an abnormal event are important. The potential disturbance propagation path in a system is drawn, and the occurrence probability in consideration of the defense system gives a present risk. The framework on safety measures aims at reduction if a risk is not acceptable, and its validity was checked in the analysis of a vaporizer system. The dynamic Bayesian network which unified the stochastic model showing the physical behavior model and component failure models which show dynamical system state transition was used for failure diagnosis, and system state presumption and diagnosis of an unusual state could be performed easily Less